Computer Simulations in the Pharmaceutical Industry : Volume 1
Book Details
Format
Paperback / Softback
Book Series
Drugs and the Pharmaceutical Sciences
ISBN-10
1041128371
ISBN-13
9781041128373
Publisher
Taylor & Francis Ltd
Imprint
CRC Press
Country of Manufacture
GB
Country of Publication
GB
Publication Date
Aug 24th, 2026
Print length
240 Pages
Ksh 9,000.00
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This volume provides comprehensive coverage of drug design, chemoinformatics, molecular modeling, and computer-aided formulation development. Computer simulations are crucial in the pharmaceutical industry, accelerating drug discovery and development by predicting drug behavior, optimizing systems, and reducing the need for animal testing.
The application of computational modeling and simulation has extended beyond PK/PD prediction of clinical trial outcomes by defining regulatory strategy as well as optimization of therapeutic candidates. Further, regulatory agencies are accepting modeling and simulation for approval in special populations, drug–drug interactions, and dosing regimen changes. This volume provides a comprehensive coverage of drug design, chemoinformatics, molecular modeling, and computer-aided formulation development. Computer simulations play a crucial role in the pharmaceutical industry, accelerating drug discovery and development by predicting drug behavior and optimizing systems, thus reducing the need for animal testing. Key FeaturesIllustrates how computer simulations play a crucial role in the pharmaceutical industry, accelerating drug discovery and development. PK/PD simulations play a crucial role in optimizing dosing regimens and understanding the time course of drug effects. Depicts the role of computers in drug research development, both in clinical and preclinical stage by citing examples of approved FDA drugs. Provides case studies detailing the step-by-step process for using software such as Python for programming in the pharmaceutical industry; Gromacs, DataWarrior, AutoDock and Biovia. Discusses how artificial intelligence and machine learning can be used to optimize and refine simulation models.
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